A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs

Farsangi, Alireza SoltaniNejad; Hadayeghparast, Shahrzad; Mehdinejad, Mehdi; Shayanfar, Heidarali

doi:10.1016/j.energy.2018.06.136

Cited by 149 publications

(19 citation statements)

References 23 publications

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“…Several studies have been of paramount importance in the optimization of the design and management of microgrids under uncertainties. Farsangi et al present in [6] a two-stage stochastic program aimed to minimize the operational cost in a microgrid with electrical and thermal loads. The uncertainties covered are renewable generation, load and electricity price, and scenarios are generated based on theoretical probability distributions such as Normal or Beta.…”

Section: Review Of the State Of The Art Justification And Objectivesmentioning

confidence: 99%

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

et al. 2020

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As renewable energy installation costs decrease and environmentally-friendly policies are progressively applied in many countries, distributed generation has emerged as the new archetype of energy generation and distribution. The design and economic feasibility of distributed generation systems is constrained by the operation of the microgrid, which has to consider the uncertainty of renewable energy sources, consumption habits and electricity market prices. In this paper, a mathematical model intended to optimize the design and economic feasibility of a microgrid is proposed. After a search in the state-of-the-art, weaknesses and strengths of existing models have been identified and taken into account for building the present model. The present model should be seen as a basis on which other models can be built upon, hence a complete definition of the different sub-models is stated: uncertainty modelling, optimization technique, physical constraints and regulatory framework. One of the main features presented is the generation of synthetic data in uncertainty modelling, employed to enhance the reliability of the model by taking into account a longer time horizon and a shorter time step. Results show significant details about energy management and prove the suitability of using a stochastic approach rather than deterministic or intuitive ones to perform the optimization.

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Section: Review Of the State Of The Art Justification And Objectivesmentioning

confidence: 99%

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

et al. 2020

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“…In this case, we need Constraint (13), which also imposes an upper bound on the temperature. Note that T t room in (9) comes from the HVAC model.…”

Section: Water Heaters Modelmentioning

confidence: 99%

A realistic energy optimization model for smart‐home appliances

2019

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Summary Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. The control of smart appliances can be based on optimization models, which should be realistic and efficient. However, increased realism also implies an increase in solution time. Many of the optimization models in the literature have limitations on the types of appliances considered and/or their reliability. This paper proposes a home energy management scheduling model that is more realistic and efficient. We develop a mixed integer linear optimization model that minimizes the energy cost while maintaining a given level of user comfort. Our main contribution is the variety of specific appliance models considered and their integration into a single model. We consider the use of energy in appliances and electric vehicles (EVs) and take into account renewable local generation, batteries, and demand response. Our models of a shower, a fridge, and a hybrid EV consider both the electricity consumption and the conventional fuel cost. We present computational results to validate the model and indicate how it overcomes the limitations of other models. Our results, compared with the best competitors, provide cost savings ranging from 8% to 389% over a horizon of 24 hours.

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“…F CHP_I (t) = a CHP_I P CHP_I (t), (8) where the parameters c CHP_I and a CHP_I are considered constant, H CHP_I (t) and P CHP_I (t) are the generated heat and electricity power, and F CHP_I (t) is the power of the consumed natural gas.…”

Section: Modelling Of Combined Heat and Power Unitsmentioning

confidence: 99%

“…[7], a multi-objective optimization algorithm was proposed for minimization of energy cost and emissions for residential microgrids considering combined cooling heating and power (CCHPs), electric and thermal energy storage and EVs. A two-stage stochastic programing was used to minimize the operational cost of a microgrid by using scenario generation and a reduction technique [8]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

2019

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Recently, the increasing integration of electric vehicles (EVs) has drawn great interest due to its flexible utilization; moreover, environmental concerns have caused an increase in the application of combined heat and power (CHP) units in multi-energy systems (MES). This paper develops an approach to coordinated scheduling of MES considering CHPs, uncertain EVs and battery degradation based on model predictive control (MPC), aimed at achieving the most economic energy scheduling. After exploiting the pattern of the drivers’ commuting behavior, the stochastic characteristics of available charging/discharging electric power of aggregated EVs in office or residential buildings are analyzed and represented by the scenarios with the help of scenario generation and reduction techniques. At each step of MPC optimization, the solution of a finite-horizon optimal control is achieved in which a suitable number of available EVs scenarios is considered, while the economic objective and operational constraints are included. The simulation results obtained are encouraging and indicate both the feasibility and the effectiveness of the proposed approach.

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A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs

Cited by 149 publications

References 23 publications

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

A realistic energy optimization model for smart‐home appliances

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

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